Lead Scientist · Apodex

Lingfeng Shen 沈凌峰

Reinforcement learning, agents, and AI for scientific discovery.

Mountain View, CA

Owl artwork, avatar of Lingfeng Shen

About

I am a Lead Scientist at Apodex, responsible for our research in reinforcement learning and AI for science.

Previously, I was a Member of Technical Staff on Microsoft AI’s MAI Superintelligence Team, working on agentic foundation models, and a Research Scientist at ByteDance Seed, where I focused on reasoning, planning, and function calling in large language models.

I hold an M.S. in Computer Science from Johns Hopkins University, where I worked with Daniel Khashabi and Philipp Koehn, and a B.S. in Applied Mathematics from China Agricultural University. I spent my final undergraduate year as a research intern at Tencent AI Lab, mentored by Haiyun Jiang, Lemao Liu, and Shuming Shi.

Beyond research, I am a professional trader focused on event-driven strategies and risk hedging, and I occasionally write about markets on my blog.

I am always glad to talk about research and collaboration. You can reach me at lingfeng [at] apodex.com or lshen30 [at] jh.edu.

Research

  • 01

    AI for finance

    Machine learning for financial markets, from forecasting and trading to risk management.

  • 02

    AI for scientific discovery

    Applying AI to open scientific questions whose answers are not yet known.

  • 03

    Agentic RL

    Training language-model agents to reason, plan, and use tools through reinforcement learning.

Earlier research topics
  • Emergent behaviors of foundation models, such as in-context learning (ICML 2024)
  • Data-centric design of foundation models (ICLR 2024)
  • Adversarial robustness (ICLR 2023, AAAI 2022)
  • Backdoor robustness and its evaluation (arXiv 2022)
  • Evaluation metrics for text generation (EMNLP 2022)

Experience

  1. Jul 2026 – Present
    Lead Scientist

    Focus on reinforcement learning and AI for science.

  2. Jan 2026 – Jul 2026
    Member of Technical Staff

    Focus on agentic foundation models.

  3. May 2024 – Dec 2025
    Research Scientist

    Focus on reasoning, planning, and function calling in large language models.

  4. Sep 2021 – Jun 2022
    Research Intern
    Tencent AI Lab

    Mentored by Haiyun Jiang, Lemao Liu, and Shuming Shi.

Education

  1. Aug 2022 – Dec 2023
    M.S. in Computer Science
    Johns Hopkins University

    Research with Daniel Khashabi and Philipp Koehn.

  2. Sep 2018 – Jun 2022
    B.S. in Applied Mathematics
    China Agricultural University

Publications

Machine learning

  1. Position: Do pretrained Transformers Learn In-Context by Gradient Descent?

    Lingfeng Shen*, Aayush Mishra*, Daniel Khashabi

    ICML 2024OralPaper
  2. The Trickle-down Impact of Reward Inconsistency on RLHF

    Lingfeng Shen, Sihao Chen, Linfeng Song, Lifeng Jin, Baolin Peng, Haitao Mi, Daniel Khashabi, Dong Yu

    ICLR 2024Paper
  3. The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

    Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi

    ACL 2024FindingsPaper
  4. On the Evaluation Metrics for Paraphrase Generation

    Lingfeng Shen, Lemao Liu, Haiyun Jiang, Shuming Shi

    EMNLP 2022OralPaper

Quantitative finance

  1. What Happens When Institutional Liquidity Enters Prediction Markets: Identification, Measurement, and a Synthetic Proof of Concept

    Lingfeng Shen (as Shaw Dalen)

    arXiv 2026Paper
  2. Toward Black Scholes for Prediction Markets: A Unified Kernel and Market Maker’s Handbook

    Lingfeng Shen (as Shaw Dalen)

    arXiv 2025Paper

Service

Reviewer

  • ICLR2024–2026
  • NeurIPS2023–2026
  • ICML2023–2026
  • ACL2023
  • AAAI2022–2024
  • EMNLP2022–2023
  • ACL Rolling Review2022–2023

Outside Work

Away from work, I am a devoted Formula 1 fan and support the Mercedes-AMG Petronas F1 Team. I also follow the NBA (I used to be a big Houston Rockets fan) and enjoy karting with friends and video games.